The standard ARIMA (autoregressive integrated moving average) model allows to make forecasts based only on the past values of the forecast variable. The model assumes that future values of a variable linearly depend on its past values, as well as on the values of past (stochastic) shocks. The ARIMAX model is an extended version of […]

# forecasting

## Forecasting for small business Exercises (Part-4)

Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of demand. In this context, it is especially important to be able to predict accurately the change in the markets […]

## Forecasting: Multivariate Regression Exercises (Part-4)

In the previous exercises of this series, forecasts were based only on an analysis of the forecast variable. Another approach to forecasting is to use external variables, which serve as predictors. This set of exercises focuses on forecasting with the standard multivariate linear regression. Running regressions may appear straightforward but this method of forecasting is […]

## Forecasting for small business Exercises (Part-3)

Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of demand. In this context, it is especially important to be able to predict accurately the change in the markets […]

## Forecasting: Exponential Smoothing Exercises (Part-3)

Exponential smoothing is a method of finding patterns in time series, which can be used to make forecasts. In its simple form, exponential smoothing is a weighted moving average: each smoothed value is a weighted average of all past time series values (with weights decreasing exponentially from the most recent to the oldest values). In […]

## Forecasting: Linear Trend and ARIMA Models Exercises (Part-2)

There are two main approaches to time series forecasting. One of them is to find persistent patterns in a time series itself, and extrapolate those patterns. Another approach is to discover how a series depend on other variables, which serve as predictors. This set of exercises focuses on the first approach, while the second one […]

## Forecasting for small business Exercises (Part-1)

Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of the demand. In this context, it is especially important to be able to predict accurately the change in the […]

## Forecasting: Time Series Exploration Exercises (Part-1)

R provides powerful tools for forecasting time series data such as sales volumes, population sizes, and earthquake frequencies. A number of those tools are also simple enough to be used without mastering sophisticated underlying theories. This set of exercises is the first in a series offering a possibility to practice in the use of such […]